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2024 IEEE International Conference on computing, ICOCO 2024

作者： Mansor, Mahayaudin M. Ibrahim, Nurain Radi, Noor Fadhilah Ahmad Rahim, Nadirah Abdul Yahaya, Syarul Heiry Bakar, Mohd Aftar Abu Zakaria, Roslinazairimah School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Malaysia Institute of Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Malaysia Department of Electrical and Computer Engineering Kuliyyah of Engineering International Islamic University Malaysia Department of Science Computer and Mathematics Universiti Teknologi MARA Penang Campus Malaysia Department of Mathematical Sciences Faculty of Science & Technology Universiti Kebangsaan Malaysia Centre for Mathematical Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Malaysia

ISBN: (纸本)9798331530303

As artificial intelligence (AI) advances, it is essential to continuously comprehend its limitations to optimise the integration of AI into autonomous systems that empower humans. The first objective of this study is to highlight the key capabilities of both humans and AI, focusing on the differences and identifying limitations of AI from the literature. Through discrepancy analysis, this study uses a Venn diagram to visualise the remaining limitations of AI into five key domains: (i) emotional intelligence, (ii) consciousness and awareness, (iii) creative imagination, (iv) communication, and (v) ethical decision-making. Furthermore, this study employs thematic bibliometric analysis to provide a more detailed examination of each AI limitation as the second objective. This study has identified underdeveloped and emerging research themes with potential for future development, such as emotion recognition, human-computer interaction, digital health, and situational awareness, which may require further research. Additionally, this study commends the ongoing efforts to harness AI’s computational power and algorithmic innovations to enhance AI’s overall performance and applicability. ©2024 IEEE.

关键词： Emotion Recognition

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Zero-shot multi-speaker accent TTS with limited accent data

Zero-shot multi-speaker accent TTS with limited accent data

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Mingyang Zhang Yi Zhou Zhizheng Wu Haizhou Li Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering National University of Singapore

In this paper, we present a multi-speaker accent speech synthesis framework. It can generate accented speech of unseen speakers using only a limited amount of accent training data. Without relying on the accent lexicon, the proposed network is able to learn the accent phoneme embedding via a simple model adaptation. In specific, a standard multi-speaker speech synthesis is first trained with native speech. Then, an additional neural network module is appended for adaptation to map the native speech to the accented speech. In the experiments, we have synthesized English speech with Singapore and Hindi accents. Both objective and subjective evaluation results successfully confirm that our proposed technique with phoneme mapping is effective to generate high-quality accent speech for unseen speakers.

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Sustainability Forecasting for Deep Learning Packages

Sustainability Forecasting for Deep Learning Packages

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IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Junxiao Han Yunkun Wang Zhongxin Liu Lingfeng Bao Jiakun Liu David Lo Shuiguang Deng School of Computer & Computing Science Hangzhou City University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China School of Computing and Information Systems Singapore Management University Singapore Singapore

ISBN: (数字)9798350330663

ISBN: (纸本)9798350330670

Deep Learning (DL) technologies have been widely adopted to tackle various tasks. In this process, through software dependencies, a multi-layer DL supply chain (SC) is formed, with DL frameworks acting as the root, DL packages acting as the bridge nodes, and downstream DL projects acting as the periphery. However, most Open Source Software (OSS) projects may fail. Considering the crucial position of DL packages in the DL SC, to foster the sustainable development of DL SCs and DL packages, we aim to forecast the long-term sustainability of DL packages. Here, sustained activity is adopted as the main proxy of sustainability, and the sustainability status is classified as “sus-tainable” or “dormant”. Relatedly, a DL package is considered as “sustainable” if it has sustained activity in its last 12 months. Otherwise, it is deemed as “dormant”. To this end, we propose an approach that begins with obtaining longitudinal features for each DL package in each month. Then, we develop a model to forecast the sustainability of DL packages by incorporating the longitudinal features, which can aptly predict sustainability with an accuracy of up to 0.81. Subsequently, an interpretable module is developed to interpret the determinants (i.e., important features) that impact the sustainability of DL packages. Finally, we generate sustainability trajectories for each DL package to better understand the monthly changes of their sustainability status. Our findings uncover that for most DL packages, fewer but more centralized developers and a balanced collaboration are more likely to help sustain the DL packages. Furthermore, although some DL packages are sustainable, their sustainability trajectories present statistically decreasing trends over time. Based on the findings, we shed light on the dynamic sustainability of DL packages, highlight future research directions, and provide practical suggestions to DL package maintainers, developers, users, and software engineering researchers.

关键词： Deep learning Supply chains Predictive models Market research Trajectory Sustainable development Task analysis

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Enhancing data Quality in Image Pre-processing: A Case Study on Plant Disease Classification

Enhancing Data Quality in Image Pre-processing: A Case Study...

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2023 International Conference for Technological Engineering and its Applications in Sustainable Development, ICTEASD 2023

作者： Ong, Ge Ye Abdullah, Nurzulaikha Ridzuan, Fakhitah Mustafa, Wan Azani Alzubaidi, Laith H. INTI International College Penang School of Engineering and Technology Penang Malaysia Universiti Malaysia Kelantan Faculty of Data Science and Computing Kota Bharu Malaysia Universiti Malaysia Perlis Faculty of Electrical Engineering & Technology Perlis Arau02600 Malaysia The Islamic University Najaf Faculty of Engineering Iraq

ISBN: (纸本)9798350336474

data quality plays a vital role in image pre-processing for machine learning applications. The effectiveness of building accurate and reliable models lies in the high-quality of data. Prioritizing data quality in the initial stages of the image pre-processing pipeline lays a strong foundation for subsequent machine learning stages. Therefore, this research aims to identify the appropriate steps for image pre-processing and compare the performance of machine learning model based on different pre-processing approaches. In this study, we utilized a plant diseases dataset sourced from Kaggle, comprising approximately 87,000 RGB images of both healthy and diseased crop leaves, categorized into 38 distinct classes. To significantly enhance the image quality, we implemented a range of transformation techniques, including resizing, normalization, data augmentation, and cropping. The analysis clearly indicates that implementing comprehensive pre-processing techniques enhances data quality and improves the machine learning model's classification performance. © 2023 IEEE.

关键词： Machine learning

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Heat exposure and hospitalizations for chronic kidney disease in China: a nationwide time series study in 261 major Chinese cities

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Military Medical Research 2024年第4期11卷 469-478页

作者： Fu-Lin Wang Wan-Zhou Wang Fei-Fei Zhang Su-Yuan Peng Huai-Yu Wang Rui Chen Jin-Wei Wang Peng-Fei Li Yang Wang Ming-Hui Zhao Chao Yang Lu-Xia Zhang Institute of Medical Technology Peking University Health Science CenterBeijing 100191China National Institute of Health Data Science at Peking University Beijing 100191China Department of Occupational and Environmental Health Sciences School of Public HealthPeking UniversityBeijing 100191China Renal Division Department of MedicinePeking University First HospitalPeking University Institute of NephrologyBeijing 100034China Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases Chinese Academy of Medical SciencesBeijing 100034China Advanced Institute of Information Technology Peking UniversityHangzhou 311215China National Climate Center China Meteorological AdministrationBeijing 100081China Peking-Tsinghua Center for Life Sciences Beijing 100034China

Background:Climate change profoundly shapes the population health at the global ***,there was still insufficient and inconsistent evidence for the association between heat exposure and chronic kidney disease(CKD).Methods:In the present study,we studied the association of heat exposure with hospitalizations for cause-specific CKD using a national inpatient database in China during the study period of hot season from 2015 to *** time-series regression models and random-effects Meta-analysis were developed to estimate the city-specific and national averaged associations at a 7 lag-day span,***:A total of 768,129 hospitalizations for CKD was recorded during the study *** results showed that higher temperature was associated with elevated risk of hospitalizations for CKD,especially in sub-tropical *** a 1℃ increase in daily mean temperature,the cumulative relative risks(RR)over lag 0-7 d were 1.008[95% confidence interval(CI)1.003-1.012]for *** attributable fraction of CKD hospitalizations due to high temperatures was 5.50%.Stronger associations were observed among younger patients and those with obstructive *** study also found that exposure to heatwaves was associated with added risk of hospitalizations for CKD compared to non-heatwave days(RR=1.116,95%CI 1.069-1.166)above the effect of daily mean ***:Short-term heat exposure may increase the risk of hospitalization for *** findings provide insights into the health effects of climate change and suggest the necessity of guided protection strategies against the adverse effects of high temperatures.

关键词： Chronic kidney disease Hospitalization Climate change Temperature Time-series study

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Enhancing Stochastic Optimization for Statistical Efficiency Using ROOT-SGD with Diminishing Stepsize

arXiv

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arXiv 2024年

作者： Zhang, Tong Li, Chris Junchi Siebel School of Computing and Data Science University of Illinois UrbanaIL United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA United States

In this paper, we revisit ROOT-SGD, an innovative method for stochastic optimization to bridge the gap between stochastic optimization and statistical efficiency. The proposed method enhances the performance and reliability of ROOT-SGD by integrating a carefully designed diminishing stepsize strategy. This approach addresses key challenges in optimization, providing robust theoretical guarantees and practical benefits. Our analysis demonstrates that ROOT-SGD with diminishing stepsize achieves optimal convergence rates while maintaining computational efficiency. By dynamically adjusting the learning rate, ROOT-SGD ensures improved stability and precision throughout the optimization process. The findings of this study offer valuable insights for developing advanced optimization algorithms that are both efficient and statistically robust. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimization

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Improving Generalization and Convergence by Enhancing Implicit Regularization

arXiv

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arXiv 2024年

作者： Wang, Mingze Wang, Jinbo He, Haotian Wang, Zilin Huang, Guanhua Xiong, Feiyu Li, Zhiyu Weinan, E. Wu, Lei School of Mathematical Sciences Peking University China Center for Machine Learning Research Peking University China China AI for Science Institute China School of Data Science University of Science and Technology of China China ByteDance Research China

In this work, we propose an Implicit Regularization Enhancement (IRE) framework to accelerate the discovery of flat solutions in deep learning, thereby improving generalization and convergence. Specifically, IRE decouples the dynamics of flat and sharp directions, which boosts the sharpness reduction along flat directions while maintaining the training stability in sharp directions. We show that IRE can be practically incorporated with generic base optimizers without introducing significant computational overload. Experiments show that IRE consistently improves the generalization performance for image classification tasks across a variety of benchmark datasets (CIFAR-10/100, ImageNet) and models (ResNets and ViTs). Surprisingly, IRE also achieves a 2× speed-up compared to AdamW in the pre-training of Llama models (of sizes ranging from 60M to 229M) on datasets including Wikitext-103, Minipile, and Openwebtext. Moreover, we provide theoretical guarantees, showing that IRE can substantially accelerate the convergence towards flat minima in sharpness-aware minimization (SAM). Copyright © 2024, The Authors. All rights reserved.

关键词： Image classification

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AttentionFaultFormer: An attention-enhanced 3D CNN & transformer model for seismic fault detection

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Journal of Applied Geophysics 2025年 238卷

作者： Wang, Jing Ma, Siteng Liu, Yue Dong, Ruihai School of Computer Science and Engineering North China Institute of Aerospace Engineering China The Insight Centre for Data Analytics School of Computer Science University College Dublin Dublin Ireland

In seismic exploration, accurately identifying faults is fundamental for interpreting seismic data. Due to the highly sparse distribution of faults in the seismic volume and the heavy reliance on local features to reconstruct fault segmentation masks, predicting faults using 3D Vision Transformers (ViT) remains challenging. We propose a novel attention-enhanced 3D U-shaped CNN and ViT hybrid model, called AttentionFaultFormer, specifically for fault detection tasks. AttentionFaultFormer includes a transformer-based encoder, a residual-based decoder, and attention-enhanced skip connection. The skip connection integrates local feature extraction from convolutional layers with channel and spatial attention mechanisms to enhance the representation of detailed fault features. Furthermore, based on the geometric structure of faults, we designed the Multi-Axis Striped Convolution Attention (MASCA) module and incorporated it into the shallow skip connections to help the model delineate continuous long faults. We applied our model to three field seismic datasets (F3, Kerry3D, and Thebe). Comparative analysis against two CNN models and two ViT models reveals that our model predicts more continuous and interpretable faults. © 2025

关键词： Attention mechanism CNN Fault detection Seismic interpretation Vision transformer

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Hypoformer: Hybrid Decomposition Transformer for Edge-friendly Neural Machine Translation

Hypoformer: Hybrid Decomposition Transformer for Edge-friend...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Li, Sunzhu Zhang, Peng Gan, Guobing Lu, Xiuqing Wang, Benyou Wei, Junqiu Jiang, Xin College of Intelligence and Computing Tianjin University Tianjin China School of Data Science The Chinese University of HongKong Shenzhen China The Hong Kong Polytechnic University Hong Kong Huawei Noah's Ark Lab China

Transformer has been demonstrated effective in Neural Machine Translation (NMT). However, it is memory-consuming and time-consuming in edge devices, resulting in some difficulties for real-time feedback. To compress and accelerate Transformer, we propose a Hybrid Tensor-Train (HTT) decomposition, which retains full rank and meanwhile reduces operations and parameters. A Transformer using HTT, named Hypoformer, consistently and notably outperforms the recent lightweight SOTA methods on three standard translation tasks under different parameter and speed scales. In extreme low resource scenarios, Hypoformer has a 7.1 point absolute improvement in BLEU and 1.27× speedup than the vanilla Transformer on the IWSLT'14 De-En task. © 2022 Association for Computational Linguistics.

关键词： Neural machine translation

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Energy Consumption Optimization using Big data Analytics for Smart Grid Management 5

Energy Consumption Optimization using Big Data Analytics for...

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5th International Conference on Emerging Systems and Intelligent computing, ESIC 2025

作者： Devi, K. Sumithra Contractor, Dipenkumar Deepa, V. Lakshmi, T.K. Santhikala, M. Mounika, Kamati Kcg College of Technology Department of Computer Science and Engineering Chennai India Government Engineering College Department of Computer Engineering Dahod India Sri Ramakrishna College of Arts and Science Department of Information Technology Tamil Nadu Coimbatore India Malla Reddy University Department of Computer Science and Engineering Telangana Hyderabad India Ramco Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Rajapalayam India Mb School of Pharmaceutical Sciences Mohan Babu University Department of Pharmacy Practice Andhra Pradesh Tirupathi India

ISBN: (纸本)9798331522100

The application of big data analytics to improve energy usage efficiency in smart grids has been prompted by the rising need for power management. Adding to the above, it poses a big data solution for demand prediction, load consolidation, and fault diagnosis. Establishing the new and state-of-the-art innovative LSTM network that was employed in the forecast yielded a higher demand forecast accuracy of 94.2% compared to the previous systems. The load balancing index achieved 92% with an accuracy of 93.7% in fault detection, thus minimizing maintenance cost by 14.7% while improving grid reliability. The proposed model was found to give better results than previous conventional machine learning techniques and even modern ones which are more complex, especially for an ever-changing grid environment. Overall, these findings show the promise of big data solutions for handling the challenges of the contemporary smart grid while improving its efficiency through energy optimization. Consequently, this work lays a framework for subsequent studies on the remaining applications of advanced analytics in energy management systems. © 2025 IEEE.

关键词： Smart power grids

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